mle                   package:mle                   R Documentation

_M_a_x_i_m_u_m _l_i_k_e_l_i_h_o_o_d _e_s_t_i_m_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Estimate parameters by the method of maximum likelihood.

_U_s_a_g_e:

     mle(minuslogl, start = formals(minuslogl), method = "BFGS", fixed = list(), ...)

_A_r_g_u_m_e_n_t_s:

minuslogl: Function to calculate negative log-likelihood

   start: Named list. Initial values for optimizer

  method: Optimization method to use. See 'optim'

   fixed: Named list. Parameter values to keep fixed during
          optimization

     ...: Further arguments to pass to 'optim'

_D_e_t_a_i_l_s:

     The 'optim' optimizer is used to find the minimum of the negative
     log-likelihood. An approximate covariance matrix for the
     parameters is obtained by inverting the Hessian matrix at the
     optimum.

_V_a_l_u_e:

     An object of class '"mle"'

_N_o_t_e:

     Be careful to note that the argument is -log L (not -2 log L). It
     is for the user to ensure that the likelihood is correct, and that
     asymptotic likelihood inference is valid.

_S_e_e _A_l_s_o:

     'mle-class'

_E_x_a_m_p_l_e_s:

     x <- 0:10
     y <- c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8)
     ll <- function(ymax=15,xhalf=6)
         -sum(dpois(y,lambda=ymax/(1+x/xhalf),log=TRUE))
     mle(ll)
     mle(ll,fixed=list(xhalf=6))

